4 Answers
4

Learning by doing is my preferred way. And when it comes to spatial statistics, R is getting seriously powerful tool. So if this is an option browse through some course materials, download the data and try it yourself.

Python might be another alternative. PySAL is an actively developed and well documented library that will let you implement all of GeoDa functionality, including SA (and most likely, even more). Python and Postgres are usually good friends so investing some time you could most likely marry those two as well.

[Moran's I] is really nothing more than Pearsons Correlation Coefficient tricked into a spatial context

. . . meaning that a basic contiguity test can produce a credible matrix and evaluation. I've tested this on my own data and found it to produce really-similar results to other Moran's I implementations.

Apologies for the double answer here, but since posting my first suggestion I came across a more-comprehensive toolkit for doing all sorts of analyses like this (including both global and local Moran's I):